Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …
intelligent transportation. Through physical technologies, applications, protocols, and …
[HTML][HTML] Connected vehicle as a mobile sensor for real time queue length at signalized intersections
With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the
connected vehicle is capable of sensing a great deal of useful traffic information, such as …
connected vehicle is capable of sensing a great deal of useful traffic information, such as …
A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction
The information of network-wide future traffic speed distribution and its propagation is
beneficial to develop proactive traffic congestion management strategies. However …
beneficial to develop proactive traffic congestion management strategies. However …
Network traffic prediction based on diffusion convolutional recurrent neural networks
By predicting the traffic load on network links, a network operator can effectively pre-dispose
resource-allocation strategies to early address, eg, an incoming congestion event. Traffic …
resource-allocation strategies to early address, eg, an incoming congestion event. Traffic …
Relation structure-aware heterogeneous graph neural network
Heterogeneous graphs with different types of nodes and edges are ubiquitous and have
immense value in many applications. Existing works on modeling heterogeneous graphs …
immense value in many applications. Existing works on modeling heterogeneous graphs …
[PDF][PDF] A comprehensive study of speed prediction in transportation system: From vehicle to traffic
In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …
urban management, such as real-time route guidance and congestion alleviation …